1 / 3

Data Warehouse Architectures Explained

Data Warehouse architectures depend upon the info requirements of the organization. A previous post on Data warehouse architecture covered a summary of the most used architecture and its components.

bhanu2149
Télécharger la présentation

Data Warehouse Architectures Explained

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Data Warehouse Architectures Explained Data Warehouse architectures depend upon the info requirements of the organization. A previous post on Data warehouse architecture covered an summary of the mostly used architecture and its components. This post will cover the possible architectures which will be wont to design a knowledge warehouse. Below mentioned are the three architectures which will be used. Basic Data Warehouse Architecture In the basic architecture, ETL process will extract the info from different source applications and directly store the info within the data warehouse after doing alterations to the info as needed . As shown within the image above, data warehouse within the center has three differing types of knowledge stored. Meta Data - Meta data is employed to explain the info . they're also available within the source applications and may be used an equivalent data. However, since data warehouse uses its unique format, meta data of the source applications can't be directly used and tiny modification like more describing is required to store meta data within the data warehouse. Raw data - data is that the data extracted and stored from operational applications. These data are in considerably detailed structure and may be wont to generate aggregated informative information. data can have sizable amount of records. Summary data - Summary data are the most vital within the data warehouse. it's required to get these summary data and store within the data warehouse for straightforward retrieval by business intelligence applications. Processing data consumes time as lot of knowledge are used with lot of processing and transformations which may cause delays in generating the specified reports. In such situations, processing the info beforehand and storing the generated summaries are highly valuable in order that those are often directly accessed. In the basic architecture, users and BI applications are directly accessing the info warehouse itself to urge the specified information to get their reports.

  2. Data Warehouse Architecture with area As Shown within the image above, the difference during this architecture is that there's a area between the source applications and therefore the data warehouse. during this architecture, the ETL processes extract the info from the operational applications and store them temporarily within the area . While the info is in area , ETL processes will transform the info as needed before inserting to the info warehouse. Use of this method is sort of easy and efficient instead of performing the transformations on the fly without having an area to store the info . Once the info on the area is prepared to be inserted to the info warehouse, again an ETL process will run to insert the info to the info warehouse. Data warehouse are going to be almost like the essential architecture. Users also will directly hook up with the info warehouse to urge the info they have similarly to the essential architecture described above. Data Warehouse Architecture with Data Marts This is subsequent architecture that has the area and also data marts. This architecture is sort of complex in comparison with the remainder . during this architecture, ETL processes will extract data from operational systems and store within the area for transformations then the info are going to be inserted to the info warehouse. After the info is inserted to the info warehouse, the info marts are going to be updated using the info within the data warehouse. Data mart may be a subset of a knowledge

  3. warehouse addressing only a selected need. Data marts also are created for specific user groups of the organization like different departments, work groups etc as they need different data requirements and that they don't require access to all or any the company's data. Users can hook up with these data marts and access the info as they require. For more information About this course Please go through this link ETL Testing Online Training Contact Information USA: +1 7327039066 INDIA: +91 8885448788, 9550102466 Email: info@onlineitguru.com

More Related